Gilles Allali1, Emmeline I Ayers2, Joe Verghese2. 1. Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, New York. Department of Clinical Neurosciences, Geneva University Hospitals and University of Geneva, Switzerland. gilles.allali@einstein.yu.edu. 2. Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, New York.
Abstract
BACKGROUND: The motoric cognitive risk (MCR) syndrome, characterized by slow gait and cognitive complaints, is a simple and easily accessible clinical approach to identify older adults at high risk for transitioning to dementia. This study aims to define subtypes of MCR based on individual quantitative gait variables and to compare their neuropsychological profiles and risk factors as well risk for incident cognitive impairment. METHODS: MCR was diagnosed in 314 community-residing, nondemented, older adults aged 65 and older (56% women) based on the presence of cognitive complaints and slow gait velocity (MCRv). Four new subtypes of MCR were defined by substituting slow gait with short stride length (MCRsl), slow swing time (MCRsw), high stride length variability (MCRslv), and high swing time variability (MCRswv). MCR subtypes were not mutually exclusive. RESULTS: A total of 25 participants (8%) met criteria for MCRv, 20 for MCRsl (6.4%), 15 for MCRsw (4.8%), 16 for MCRslv (5.1%), 12 for MCRswv (3.8%), and 266 participants (84.7%) did not meet criteria for any MCR subtype. At baseline, MCRv was associated with deficits in attention and language as well as in overall cognitive status. MCRswv was associated with deficits in all cognitive domains including memory. Obesity and sedentariness were risk factors of MCRv, MCRsl, and MCRsw. MCRv status predicted incident cognitive impairment in global cognition (odds ratio: 3.59, p = .016), whereas MCRswv status predicted incident cognitive impairment in memory (odds ratio: 4.24, p = .048). CONCLUSIONS: MCR subtypes based on individual gait parameters show commonalities and differences in cognitive profiles and risk factors. Future studies should investigate whether the MCR subtypes predict different subtypes of dementia.
BACKGROUND: The motoric cognitive risk (MCR) syndrome, characterized by slow gait and cognitive complaints, is a simple and easily accessible clinical approach to identify older adults at high risk for transitioning to dementia. This study aims to define subtypes of MCR based on individual quantitative gait variables and to compare their neuropsychological profiles and risk factors as well risk for incident cognitive impairment. METHODS: MCR was diagnosed in 314 community-residing, nondemented, older adults aged 65 and older (56% women) based on the presence of cognitive complaints and slow gait velocity (MCRv). Four new subtypes of MCR were defined by substituting slow gait with short stride length (MCRsl), slow swing time (MCRsw), high stride length variability (MCRslv), and high swing time variability (MCRswv). MCR subtypes were not mutually exclusive. RESULTS: A total of 25 participants (8%) met criteria for MCRv, 20 for MCRsl (6.4%), 15 for MCRsw (4.8%), 16 for MCRslv (5.1%), 12 for MCRswv (3.8%), and 266 participants (84.7%) did not meet criteria for any MCR subtype. At baseline, MCRv was associated with deficits in attention and language as well as in overall cognitive status. MCRswv was associated with deficits in all cognitive domains including memory. Obesity and sedentariness were risk factors of MCRv, MCRsl, and MCRsw. MCRv status predicted incident cognitive impairment in global cognition (odds ratio: 3.59, p = .016), whereas MCRswv status predicted incident cognitive impairment in memory (odds ratio: 4.24, p = .048). CONCLUSIONS: MCR subtypes based on individual gait parameters show commonalities and differences in cognitive profiles and risk factors. Future studies should investigate whether the MCR subtypes predict different subtypes of dementia.
Authors: Inge Leunissen; James P Coxon; Karen Caeyenberghs; Karla Michiels; Stefan Sunaert; Stephan P Swinnen Journal: Hum Brain Mapp Date: 2013-08-02 Impact factor: 5.038
Authors: Panayotes Demakakos; Rachel Cooper; Mark Hamer; Cesar de Oliveira; Rebecca Hardy; Elizabeth Breeze Journal: PLoS One Date: 2013-07-09 Impact factor: 3.240
Authors: Joe Verghese; Cuiling Wang; David A Bennett; Richard B Lipton; Mindy J Katz; Emmeline Ayers Journal: Alzheimers Dement Date: 2019-06-01 Impact factor: 21.566
Authors: Keith A Shaughnessy; Kyle J Hackney; Brian C Clark; William J Kraemer; Donna J Terbizan; Ryan R Bailey; Ryan McGrath Journal: J Alzheimers Dis Date: 2020 Impact factor: 4.472
Authors: Vijay R Varma; Jeffrey M Hausdorff; Stephanie A Studenski; Caterina Rosano; Richard Camicioli; Neil B Alexander; Wen G Chen; Lewis A Lipsitz; Michelle C Carlson Journal: J Gerontol A Biol Sci Med Sci Date: 2016-05-06 Impact factor: 6.053